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A mathematical approach to radiographicimage diagnosis -cystic lesions-

射线照相图像诊断的数学方法 - 囊性病变 -

基本信息

批准号：
06671897
负责人：
KOSEKI Takakazu
金额：
$ 1.34万
依托单位：
Osaka Dental University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

项目摘要

Accurate diagnosis of oral lesions can be made only by corn-prehensive evaluation of a large quantity of information including in clinical signs and symptoms, and the past history as well as findings on various examinations including radiographical and histopathological examinations. Clinical experience over many years is needed for a physician to become able to select from a large volume of information and utilize only that which is useful However, if the accumulated information can be classified in a more utilizable and accessible forms and can be quickly referred to concerning the case in question, it would, to some extent, diminish the difference in experience among individual doctors and improve the accuracy of the diagnosis.We input the age, sex, site of the lesion, disease, and image data of patients as basic data, and calculated the probability of the correct diagnosis in computer-aided medical decision making based on these data. Data of 13,036 of the 15,235 patients who were … More examined at the Department of Oral Radiology, Osaka Dental University during the 8 years between 1987 and 1992 was classified and used as basic data.Ten disease categories (68 lesions), i.e., inflammation, odontogenic cysts, non-odontogenic cysts, odontogenic tumors, non-odontogenic tumors, malignant tumors, trauma, TMJ disorders, unclassifiable diseases, and others were established. The sites of the lesions were classified into 7 categories (32 sites). Radiographic images were classified into 6 types (18 patterns). Radiographic images were most frequently radiolucencies of the jaws, which were observed in 64% of the patients, and inflammatlon accounted for 86%. No changes, which were the next most frequent radiographic images, were observed in the jaw bones in 14%.We derived a symptom-disease matrix of intra-osseous lesions from these data and calculated the posterior probability and the statical decision tried to radiographic findings from histopathological type of ameloblastoma (289 cases) from these data and calculated the posterior probability.The results of posterior probability were almost correct. But, the unusual radiographic finding case was not correct. These posterior probability are considered to assist interpretation of radiographic images for medical decision making. Less

口腔病变的准确诊断需要综合临床体征、症状、既往病史及影像学、组织病理学等各项检查结果等大量信息。医生需要多年的临床经验，才能从大量的信息中进行选择，并只利用有用的信息。然而，如果积累的信息能够以更易于利用和访问的形式进行分类，并能够快速地参考所讨论的病例，这将在一定程度上减少医生个人经验的差异，提高诊断的准确性。我们输入患者的年龄、性别、病变部位、疾病、影像数据作为基础数据，并根据这些数据计算计算机辅助医疗决策中正确诊断的概率。1987年至1992年8年间，在大阪牙科大学口腔放射科对15235名患者进行了检查，其中13036名患者的数据被分类并作为基本数据。建立了炎症、牙源性囊肿、非牙源性囊肿、牙源性肿瘤、非牙源性肿瘤、恶性肿瘤、创伤、颞下颌关节紊乱、不可分类疾病等10个疾病类别（68个病变）。病变部位分为7类（32个部位）。射线图像分为6类（18种模式）。x线影像中最常见的是颌骨的放射透光，64%的患者观察到，炎症占86%。其次最常见的x线图像是颌骨无变化，占14%。我们从这些数据中得出骨内病变的症状-疾病矩阵，并计算后验概率和静态决策，试图从这些数据中计算成釉细胞瘤（289例）的组织病理类型的影像学表现，并计算后验概率。后验概率的结果基本正确。但是，不寻常的x线片发现情况是不正确的。这些后验概率被认为有助于医学决策中放射影像的解释。少